Integrating Diverse Data Sources for Future-Proof Transport Modelling

Robin Lovelace
e.mail@leeds.ac.uk

University of Leeds

Juan Pablo Fonseca Zamora

University of Leeds

Zhao Wang

University of Leeds

Malcolm Morgan

University of Leeds

et. al.

June 26, 2025

Motivation

  • Traditional transport models rely on limited datasets, leading to biases and blind spots (Lovelace et al. 2017).
  • The ‘data revolution’ enables integration of open, proprietary, and crowdsourced data.
  • Aim: Develop models that can easily integrate new data sources for more robust, future-proof transport planning.

Schematic diagram of the 4-stage model (Boyce and Williams 2015).

Introduction

  • Historic models focus on commuting and simplified networks, missing active travel and non-work trips (Vybornova et al. 2024).
  • Recent tools often focus on either behaviour or infrastructure, limiting scope (Vierø, Vybornova, and Szell 2024).
  • The Network Planning Tool for Scotland (NPT) demonstrates a data-centric, integrative approach.

The Network Planning Tool showing the layer user interface. Source: www.npt.scot

Data Integration

  • NPT is designed to integrate a variety of data types: open, proprietary, and crowdsourced.
  • Builds on prior work (e.g., CRUSE tool (Lovelace et al. 2024)) and integrates infrastructure and behavioural data.
  • Over a dozen datasets combined for holistic transport analysis.

Prior work: screenshot of the CRUSE tool zoomed-in on Dublin. Source: https://www.cruse.bike/

Key Data Sources

  1. OpenStreetMap (OSM): Crowdsourced, detailed paths and road attributes.
  2. OS OpenRoads: Open-access, simplified UK road network.
  3. OS MasterMap Highways: Detailed road widths and features.
  4. OS MasterMap Topography: Detailed polygons for kerb lines, footways, etc.
  5. Behavioural data: Census, Scottish Household Travel Survey, British National Travel Survey.

Tools Developed and Used

  • mastermapr (R): Efficient import/processing of OS MasterMap data.
  • osmactive (R): Classifies OSM data, calculates pavement widths, identifies bus routes.
  • anime (Rust): Fast, accurate network joining and enrichment.
  • Enables harmonisation of raw, diverse datasets for NPT analysis.

Results: NPT Web Application

  • NPT web app (https://www.npt.scot) provides planners and public with insights into Scotland’s transport network.
  • Efficient rendering of large datasets (e.g., via pmtiles).
  • Multiple interactive layers for scenario modelling and planning.

Live demo

Give the tool a try at www.npt.scot!

Example Layers: Integrated Data

  • Route Network Layer: Cycling potential from OSM and OS data.
  • Infrastructure & Traffic Layer: Cycle infrastructure (from OSM, classified by osmactive) + modelled traffic.
  • Street Space Layer: Road classifications by available width (OS MasterMap).
  • Core Network Layer: Cohesive network for prioritising new infrastructure.
  • Network Planning Workspace: Interactive tool for exploring and modelling changes.

Open source classification of OSM data

Cycle infrastructure classification in Glasgow, derived from OpenStreetMap data using the osmactive package.

Street Space Layer

The Street Space layer in the NPT, showing roads categorised by available width for infrastructure.

Discussion & Implications

  • Integrating diverse data sources leads to more comprehensive, adaptable models.
  • Open-source tools and methods promote reproducibility and flexibility.
  • Key benefits: improved accuracy, multi-modal insights, transparency.
  • Ongoing challenge: managing complexity and ensuring relevance as data volume grows.

Future Work & References

  • Incorporate more datasets (e.g., Scotland’s Spatial Hub).
  • Research efficient data processing and feature selection.
  • Commitment to data diversity and robust integration is crucial for sustainable, equitable transport systems.

Credits

  • Full author list of paper: Robin Lovelace, Zhao Wang, Hussein Mahfouz, Juan Pablo Fonseca Zamora, Martin Lucas-Smith, Dustin Carlino, Angus Calder, Congying Hu, Michael Naysmith, Matthew Davis
  • Affiliations: University of Leeds, CycleStreets Ltd, A/B Street Ltd, Sustrans Scotland
  • For further info: get in touch with r. lovelace at leeds.ac.uk

References

Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). https://doi.org/10.5198/jtlu.2016.862.
Lovelace, Robin, Joey Talbot, Eugeni Vidal-Tortosa, Hussein Mahfouz, Elaine Brick, Peter Wright, Gary O’Toole, Dan Brennan, and Suzanne Meade. 2024. “Cycle Route Uptake and Scenario Estimation (CRUSE): An Approach for Developing Strategic Cycle Network Planning Tools.” European Transport Research Review 16 (1): 55. https://doi.org/10.1186/s12544-024-00668-8.
Vierø, Ane Rahbek, Anastassia Vybornova, and Michael Szell. 2024. “BikeDNA: A Tool for Bicycle Infrastructure Data and Network Assessment.” Environment and Planning B: Urban Analytics and City Science 51 (2): 512–28. https://doi.org/10.1177/23998083231184471.
Vybornova, Anastassia, Ane Rahbek Vierø, Kirsten Krogh Hansen, and Michael Szell. 2024. “BikeNodePlanner: A Data-Driven Decision Support Tool for Bicycle Node Network Planning,” December. https://doi.org/10.48550/arXiv.2412.20270.